The near-completion of the Human Genome Projects has revealed that there are approximately 30,000 to 40,000 genes in the human genome. Genetic and molecular studies have shown that each gene may be linked to other genes at various levels, such as transcriptional regulation and protein interactions. In this new era of genomic biology, single gene or protein perspectives are becoming increasingly limited for gaining insight into biological processes. Global, systemic, or network perspectives are becoming increasingly important for making progress in our understanding of the biological processes and harnessing this understanding in educated intervention for correcting human diseases. The development of high throughput genomic and proteomic technologies is empowering researchers in the collection of broad-scope gene information. However, it remains a major challenge to digest the massive amounts of information and use it in an intelligent and comprehensive manner. The development of systematic approaches to finding genes for effective therapeutic intervention requires new models and tools for understanding and managing complex genetic networks.
To understand the nature of cellular function, it is necessary to study the behavior of genes in a holistic rather than in an individual manner. Mathematical and computational methods may be developed to construct formal models of genetic interactions. This research direction provides insight and a conceptual framework for an integrative view of genetic function and regulation.
There have been a number of attempts to model gene regulatory networks, including linear models, Bayesian network models, neural network models, differential equation-based models, and models including stochastic components on the molecular level. In general, gene expression time trajectories can be modeled as random functions of time. The model system that has received, perhaps, the most attention is the Boolean Network model originally introduced in the late 1960's to early 1970's. In this model, gene expression is quantized to only two levels: ON and OFF. The expression level (state) of each gene is functionally related to the expression states of some other genes, using logical rules. Computational methods that reveal these logical interrelations have been successfully constructed.
Recent computational methods indicate that many other realistic biological questions may be answered within the seemingly simplistic Boolean formalism, which emphasizes fundamental, generic principles rather than quantitative biochemical details. Current methods have yielded insights into the overall behavior of large genetic networks and allow the study of large data sets in a global fashion. For example, the dynamic behavior of such networks can be used to model many biologically meaningful phenomena, for example, cellular state dynamics, possessing switch-like behavior, stability, and hysteresis. Additional uses of such methods may include uses such as the identification of suitable drug targets in cancer therapy by inferring the structure of the genetic models from experimental data (e.g., from gene expression profiles). Recent work has gone into identifying the structure of gene regulatory networks from expression data. It remains an open question as to the degree to which the Boolean formalism can explain the complicated genetic network interplay of higher-order eukaryotes, where more uncertainty of the network exists, attributed to increased gene complexity and differentiation-related specification.
Other methods depart from traditional deterministic constraints of Boolean models by using so-called noisy Boolean networks together with an identification algorithm, in order to deal with noise present in expression patterns. In that model, the requirement of consistency intrinsically imposed by Boolean functions are relaxed.
Limitations of standard Boolean networks include an inherent determinism. From a conceptual point of view, it is likely that the regularity of genetic function and interaction known to exist is not due to hard-wired logical rules, but rather to the intrinsic self-organizing stability of the dynamical system, despite the existence of stochastic components in the cell. Empirically, the assumption of only one logical rule per gene may lead to incorrect conclusions when inferring these rules from gene expression measurements, as the latter are typically noisy and the number of samples is small relative to the number of parameters to be inferred.